Pgvector vs. Vespa
Compare Pgvector vs. Vespa by the following set of capabilities. We want you to choose the best database for you, even if it’s not us.
Pgvector vs. Vespa on Scalability
Yes. pgvector enables separation of storage and compute by allowing you to store your application data on one database while storing vectors, lookup values, and filter values on a separate database.
Yes.
Both
pgvector scalability
You can use a solution like YugaByteDB to extend the capabilities of Postgres for distributed environments.
Vespa
Vespa is a scalable search engine with a robust distributed architecture that supports horizontal scaling by adding more nodes. It features automatic sharding and data redistribution, allowing it to efficiently manage large datasets and high query volumes.
Pgvector vs. Vespa on Functionality
Performance is the biggest challenge with vector databases as the number of unstructured data elements stored in a vector database grows into hundreds of millions or billions, and horizontal scaling across multiple nodes becomes paramount.
Furthermore, differences in insert rate, query rate, and underlying hardware may result in different application needs, making overall system tunability a mandatory feature for vector databases.
Yes (paged tensor attributes)
Yes. Sparse & Dense Vectors and Scalar filtering.
Yes, vector search & keyword seach
HNSW & IVFFlat
HNSW, Hybrid HNSW-IF (Inverted File), paged tensor attributes
Vespa
Vespa is a powerful search engine and vector database that can handle multiple searches simultaneously. It's great at vector search, text search, and searching through structured data.
Pgvector vs. Vespa on Purpose-built
pgvector is an add-on to Postgres
Yes.
Use pgvector from any language with a Postgres client
Python, Java
Pgvector vs. Vespa: what’s right for me?
Pgvector
pgvector is a PostgreSQL extension designed to facilitate the storage, querying, and indexing of vectors within a PostgreSQL database.
License: PostgreSQL License
Vespa
Vespa is a powerful search engine and vector database that can handle multiple searches simultaneously. It's great at vector search, text search, and searching through structured data.
Apache 2.0
